Supervised Probabilistic Principal Component Analysis
2006
Conference Paper
ei
Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g.,~in a classification or regression task, PCA is however not able to use this information. The problem is more interesting if only part of the input data are labeled, i.e.,~in a semi-supervised setting. In this paper we propose a supervised PCA model called SPPCA and a semi-supervised PCA model called S$^2$PPCA, both of which are extensions of a probabilistic PCA model. The proposed models are able to incorporate the label information into the projection phase, and can naturally handle multiple outputs (i.e.,~in multi-task learning problems). We derive an efficient EM learning algorithm for both models, and also provide theoretical justifications of the model behaviors. SPPCA and S$^2$PPCA are compared with other supervised projection methods on various learning tasks, and show not only promising performance but also good scalability.
Author(s): | Yu, S. and Yu, K. and Tresp, V. and Kriegel, H-P. and Wu, M. |
Book Title: | KDD 2006 |
Journal: | Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006) |
Pages: | 464-473 |
Year: | 2006 |
Month: | August |
Day: | 0 |
Editors: | Ungar, L. |
Publisher: | ACM Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1145/1150402.1150454 |
Event Name: | 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
Event Place: | Philadelphia, PA, USA |
Address: | New York, NY, USA |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @inproceedings{4069, title = {Supervised Probabilistic Principal Component Analysis}, author = {Yu, S. and Yu, K. and Tresp, V. and Kriegel, H-P. and Wu, M.}, journal = {Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006)}, booktitle = {KDD 2006}, pages = {464-473}, editors = {Ungar, L. }, publisher = {ACM Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {New York, NY, USA}, month = aug, year = {2006}, doi = {10.1145/1150402.1150454}, month_numeric = {8} } |